In jpg driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the `tf.compat.v1.signal.rfft2d` and `tf.compat.v1.signal.rfft3d` lack input validation and under certain condition can result in crashes (due to `CHECK`-failures). Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.ragged.constant` does not fully validate the input arguments. This results in a denial of service by consuming all available memory. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid. In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a potential for segfault / denial of service in TensorFlow by calling `tf.compat.v1.*` ops which don't yet have support for quantized types, which was added after migration to TensorFlow 2.x. In these scenarios, since the kernel is missing, a `nullptr` value is passed to `ParseDimensionValue` for the `py_value` argument. Then, this is dereferenced, resulting in segfault. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the macros that TensorFlow uses for writing assertions (e.g., `CHECK_LT`, `CHECK_GT`, etc.) have an incorrect logic when comparing `size_t` and `int` values. Due to type conversion rules, several of the macros would trigger incorrectly. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorDenseAdd` does not fully validate the input arguments. In this case, a reference gets bound to a `nullptr` during kernel execution. This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

In the System UI, there is a possible system crash due to an uncaught exception. This could lead to local permanent denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11Android ID: A-33646131

In camera driver, there is a possible memory corruption due to improper locking. This could lead to local denial of service in kernel.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In the settings app, there is a possible app crash due to improper input validation. This could lead to local denial of service of the Settings app with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-136005061

In the Accessibility service, there is a possible permission bypass due to an unsafe PendingIntent. This could lead to local information disclosure with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11Android ID: A-154913130

In sprd_sysdump driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.

In camera driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In libstagefright, there is a possible use-after-free due to improper locking. This could lead to local escalation of privilege in the media server with no additional execution privileges needed. User interaction is not needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-77474014

In endCallForSubscriber of PhoneInterfaceManager.java, there is a possible way to prevent access to emergency services due to a logic error in the code. This could lead to a local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.GetSessionTensor` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Improper buffer size check logic in aviextractor library prior to SMR May-2022 Release 1 allows out of bounds read leading to possible temporary denial of service. The patch adds buffer size check logic.

Improper buffer size check logic in wmfextractor library prior to SMR May-2022 Release 1 allows out of bounds read leading to possible temporary denial of service. The patch adds buffer size check logic.

TensorFlow is an end-to-end open source platform for machine learning. When a user does not supply arguments that determine a valid sparse tensor, `tf.raw_ops.SparseTensorSliceDataset` implementation can be made to dereference a null pointer. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either `indices` or `values` are provided for an empty sparse tensor when the other is not. If `indices` is empty, then [code that performs validation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If `indices` as provided by the user is empty, then `indices` in the C++ code above is backed by an empty `std::vector`, hence calling `indices->dim_size(0)` results in null pointer dereferencing (same as calling `std::vector::at()` on an empty vector). We have patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). We have patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.MapStage`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the `key` input is a valid non-empty tensor. We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

In generateCrop of WallpaperManagerService.java, there is a possible sysui crash due to image exceeding maximum texture size. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-8.0 Android-8.1 Android-9 Android-10Android ID: A-120847476

In LocaleList of LocaleList.java, there is a possible forced reboot due to an uncaught exception. This could lead to local denial of service requiring factory reset to restore with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-8.0 Android-8.1 Android-9 Android-10Android ID: A-152410253

In ContactsProvider, there is a possible crash loop due to resource exhaustion. This could lead to local persistent denial of service in the Phone app with User execution privileges needed. User interaction is not needed for exploitation.

In Policy of Policy.java, there is a possible boot loop due to resource exhaustion. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

In update of MmsProvider.java, there is a possible way to bypass file permission checks due to a race condition. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

In AccountTypePreference of AccountTypePreference.java, there is a possible way to mislead the user about accounts installed on the device due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-213905884

In multiple functions of JobStore.java, there is a possible way to cause a crash on startup due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-246542285

In multiple locations, there is a possible way to trigger a persistent reboot loop due to improper input validation. This could lead to local denial of service with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-246750467

In Usage, there is a possible permanent DoS due to resource exhaustion. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

In addNetwork of WifiManager.java, there is a possible way to trigger a persistent DoS due to resource exhaustion. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-244713323

In pushDynamicShortcut of ShortcutPackage.java, there is a possible way to get the device into a boot loop due to resource exhaustion. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-250576066

In gpu device, there is a memory corruption due to a use after free. This could lead to local denial of service in kernel.

In engineermode services, there is a missing permission check. This could lead to local denial of service in engineermode services.

In gnss driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in wlan services.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In soter service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In FM service , there is a possible missing params check. This could lead to local denial of service in FM service .

In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.